Developer Guide: Building a Multi-Agent RAG Legal Assistant Using LangGraph and FastAPI

A new beginner-oriented tutorial published on DEV Community walks developers through building an AI-powered legal assistant tailored to UAE Federal Law documents. The system uses Retrieval-Augmented Generation (RAG) to answer questions based solely on uploaded legal PDFs rather than relying on a language model's training data, reducing the risk of hallucinated legal clauses. Unlike basic RAG setups, the architecture employs a cyclic multi-agent pipeline built with LangGraph, where a fact-checker node verifies each drafted answer against the source text before it reaches the user. The full stack includes FastAPI for the backend, Streamlit for the frontend, and Pinecone as the vector database for storing and retrieving document embeddings. Although the tutorial uses UAE labor law as its example dataset, the guide notes the same architecture can be adapted to medical guidelines, company policies, or any custom document collection.